Object attributes combine additively in visual search

We perceive objects as containing a variety of attributes: local features, relations between features, internal details, and global properties. But we know little about how they combine. Here, we report a remarkably simple additive rule that governs how these diverse object attributes combine in vision. The perceived dissimilarity between two objects was accurately explained as a sum of (a) spatially tuned local contour-matching processes modulated by part decomposition; (b) differences in internal details, such as texture; (c) differences in emergent attributes, such as symmetry; and (d) differences in global properties, such as orientation or overall configuration of parts. Our results elucidate an enduring question in object vision by showing that the whole object is not a sum of its parts but a sum of its many attributes.

SALIENT OBJECT DETECTION VIA OBJECTNESS MEASURE

Salient object detection has become an important task in many image processing applications. The existing approaches exploit background prior and contrast prior to attain state of the art results. In this paper, instead of using background cues, we estimate the foreground regions in an image using objectness proposals and utilize it to obtain smooth and accurate saliency maps. We propose a novel saliency measure called `foreground connectivity' which determines how tightly a pixel or a region is connected to the estimated foreground. We use the values assigned by this measure as foreground weights and integrate these in an optimization framework to obtain the final saliency maps. We extensively evaluate the proposed approach on two benchmark databases and demonstrate that the results obtained are better than the existing state of the art approaches.

Flexible representations of dynamics are used in object manipulation.

To manipulate an object skillfully, the brain must learn its dynamics, specifying the mapping between applied force and motion. A fundamental issue in sensorimotor control is whether such dynamics are represented in an extrinsic frame of reference tied to the object or an intrinsic frame of reference linked to the arm. Although previous studies have suggested that objects are represented in arm-centered coordinates [1-6], all of these studies have used objects with unusual and complex dynamics. Thus, it is not known how objects with natural dynamics are represented. Here we show that objects with simple (or familiar) dynamics and those with complex (or unfamiliar) dynamics are represented in object- and arm-centered coordinates, respectively. We also show that objects with simple dynamics are represented with an intermediate coordinate frame when vision of the object is removed. These results indicate that object dynamics can be flexibly represented in different coordinate frames by the brain. We suggest that with experience, the representation of the dynamics of a manipulated object may shift from a coordinate frame tied to the arm toward one that is linked to the object. The additional complexity required to represent dynamics in object-centered coordinates would be economical for familiar objects because such a representation allows object use regardless of the orientation of the object in hand.

Computer generated hologram with geometric occlusion using GPU-accelerated depth buffer rasterization for three-dimensional display

We present a method of rapidly producing computer-generated holograms that exhibit geometric occlusion in the reconstructed image. Conceptually, a bundle of rays is shot from every hologram sample into the object volume.We use z buffering to find the nearest intersecting object point for every ray and add its complex field contribution to the corresponding hologram sample. Each hologram sample belongs to an independent operation, allowing us to exploit the parallel computing capability of modern programmable graphics processing units (GPUs). Unlike algorithms that use points or planar segments as the basis for constructing the hologram, our algorithm's complexity is dependent on fixed system parameters, such as the number of ray-casting operations, and can therefore handle complicated models more efficiently. The finite number of hologram pixels is, in effect, a windowing function, and from analyzing the Wigner distribution function of windowed free-space transfer function we find an upper limit on the cone angle of the ray bundle. Experimentally, we found that an angular sampling distance of 0:01' for a 2:66' cone angle produces acceptable reconstruction quality. © 2009 Optical Society of America.

Computer generated hologram from point cloud using graphics processor

Computer generated holography is an extremely demanding and complex task when it comes to providing realistic reconstructions with full parallax, occlusion, and shadowing. We present an algorithm designed for data-parallel computing on modern graphics processing units to alleviate the computational burden. We apply Gaussian interpolation to create a continuous surface representation from discrete input object points. The algorithm maintains a potential occluder list for each individual hologram plane sample to keep the number of visibility tests to a minimum.We experimented with two approximations that simplify and accelerate occlusion computation. It is observed that letting several neighboring hologramplane samples share visibility information on object points leads to significantly faster computation without causing noticeable artifacts in the reconstructed images. Computing a reduced sample set via nonuniform sampling is also found to be an effective acceleration technique. © 2009 Optical Society of America.

Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.